A trajectory is an ordered sequence of states, actions, rewards, or observations generated as an agent interacts with an environment. In reinforcement learning and robotics, trajectories describe how behavior unfolds over time and are used for learning, evaluation, or planning.
In reinforcement learning, a sequence of tuples that represent a sequence of state transitions of the agent, where each tuple corresponds to the state, action, reward, and next state for a given state transition.
A "trajectory" refers to a sequence of steps or actions taken by an agent or model. It's often used in the evaluation of generative models, where the model's ability to generate text, code, or other content is assessed. There are several types of trajectory metrics that can be used to evaluate generative models, including trajectory exact match, trajectory in-order match, trajectory any order match, and trajectory precision. These metrics measure the similarity between the model's output and a set of human-generated reference outputs.